A novel fuzzy set based multifactor dimensionality reduction method for detecting gene–gene interaction. (December 2016)

• Record Type: Journal Article
• Authors: Jung, Hye-Young; Leem, Sangseob; Lee, Sungyoung; Park, Taesung
• Is Part Of: Computational biology and chemistry. Volume 65(2016)
• Page Start: 193
• Page End: 202
• Publication Date: 2016-12
• Subjects: Gene-gene interaction -- Multifactor dimensionality reduction -- Fuzzy classifier -- Fuzzy set theory -- Uncertainty; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2016.09.006
• Languages: English
• ISSNs: 1476-9271

Abstract

Abstract: Background: Gene-gene interaction (GGI) is one of the most popular approaches for finding the missing heritability of common complex traits in genetic association studies. The multifactor dimensionality reduction (MDR) method has been widely studied for detecting GGIs. In order to identify the best interaction model associated with disease susceptibility, MDR compares all possible genotype combinations in terms of their predictability of disease status from a simple binary high(H) and low(L) risk classification. However, this simple binary classification does not reflect the uncertainty of H/L classification. Methods: We regard classifying H/L as equivalent to defining the degree of membership of two risk groups H/L. By adopting the fuzzy set theory, we propose Fuzzy MDR which takes into account the uncertainty of H/L classification. Fuzzy MDR allows the possibility of partial membership of H/L through a membership function which transforms the degree of uncertainty into a [0, 1] scale. The best genotype combinations can be selected which maximizes a new fuzzy set based accuracy measure. Results: Two simulation studies are conducted to compare the power of the proposed Fuzzy MDR with that of MDR. Our results show that Fuzzy MDR has higher power than MDR. We illustrate the proposed Fuzzy MDR by analysing bipolar disorder (BD) trait of the WTCCC dataset to detect GGI associated with BD. Conclusions: We propose a novel Fuzzy MDR method to detect gene–gene interaction by taking into account the uncertainly of H/L classification and show that it has higher power than MDR. Fuzzy MDR can be easily extended to handle continuous phenotypes as well. The program written in R for the proposed Fuzzy MDR is available athttps://statgen.snu.ac.kr/software/FuzzyMDR .